
import pandas as pd
df = pd.read_csv('creditCustomers.csv')
df.describe()


df.head()


import matplotlib.pyplot as plt
feature1 = 'Customer_Age'
feature2 = 'Total_Trans_Amt'

# Create the scatter plot
plt.figure(figsize=(8, 6))
plt.scatter(df[feature1], df[feature2], alpha=0.5, color='b')
plt.xlabel(f'{feature1}')
plt.ylabel(f'{feature2}')
plt.title(f'Scatter Plot: {feature1} vs. {feature2}')
plt.grid(True)
plt.show()


import matplotlib.pyplot as plt
feature1 = 'Customer_Age'
feature2 = 'Credit_Limit'

# Create the scatter plot
plt.figure(figsize=(8, 6))
plt.scatter(df[feature1], df[feature2], alpha=0.5, color='b')
plt.xlabel(f'{feature1}')
plt.ylabel(f'{feature2}')
plt.title(f'Scatter Plot: {feature1} vs. {feature2}')
plt.grid(True)
plt.show()


import seaborn as sns
selected_features = ['Customer_Age', 'Dependent_count', 'Months_on_book','Credit_Limit','Total_Revolving_Bal','Avg_Open_To_Buy','Total_Amt_Chng_Q4_Q1','Total_Trans_Amt','Total_Trans_Ct','Total_Ct_Chng_Q4_Q1','Avg_Utilization_Ratio']
sns.pairplot(df[selected_features])

/Users/apple/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight
  self._figure.tight_layout(*args, **kwargs)

<seaborn.axisgrid.PairGrid at 0x14777a510>


feature = 'Customer_Age'

plt.figure(figsize=(8, 6))
plt.hist(df[feature], bins=20, color='skyblue', edgecolor='black')
plt.xlabel(f'{feature}')
plt.ylabel('Frequency')
plt.title(f'Histogram: {feature}')
plt.grid(True)
plt.show()


import numpy as np
feature = 'Total_Trans_Amt'

plt.figure(figsize=(8, 6))
plt.hist(df[feature], bins=20, color='skyblue', edgecolor='black')
plt.xlabel(f'{feature}')
plt.ylabel('Frequency')
plt.title(f'Histogram: {feature}')
plt.grid(True)
plt.show()


feature = 'Total_Trans_Amt'

plt.figure(figsize=(8, 6))
plt.hist(np.log(df[feature]), bins=20, color='skyblue', edgecolor='black')
plt.xlabel(f'{feature}')
plt.ylabel('Frequency')
plt.title(f'Histogram: {feature}')
plt.grid(True)
plt.show()


import seaborn as sns
feature = 'Total_Trans_Amt'

plt.figure(figsize=(8, 6))
sns.boxplot(x=df[feature])
plt.xlabel(f'{feature}')
plt.title(f'Box Plot: {feature}')
plt.grid(True)
plt.show()


import seaborn as sns
feature = 'Customer_Age'

plt.figure(figsize=(8, 6))
sns.boxplot(x=df[feature])
plt.xlabel(f'{feature}')
plt.title(f'Box Plot: {feature}')
plt.grid(True)
plt.show()


feature = 'Total_Trans_Amt'

plt.figure(figsize=(8, 6))
sns.boxplot(x=df[feature])
plt.xlabel(f'{feature}')
plt.title(f'Box Plot: {feature}')
plt.grid(True)
plt.show()


feature = 'Avg_Open_To_Buy'

plt.figure(figsize=(8, 6))
sns.boxplot(x=df[feature])
plt.xlabel(f'{feature}')
plt.title(f'Box Plot: {feature}')
plt.grid(True)
plt.show()


feature = 'Total_Trans_Amt'
skewness = df[feature].skew()
mininum = df[feature].min()
maximum = df[feature].max()
mean = df[feature].mean()
median = df[feature].median()
print(f'Min of {feature}: {mininum:.2f}')
print(f'Max of {feature}: {maximum:.2f}')
print(f'Median of {feature}: {median:.2f}')
print(f'Mean of {feature}: {mean:.2f}')
print(f'Skewness of {feature}: {skewness:.2f}')

Min of Total_Trans_Amt: 510.00
Max of Total_Trans_Amt: 18484.00
Median of Total_Trans_Amt: 3899.00
Mean of Total_Trans_Amt: 4404.09
Skewness of Total_Trans_Amt: 2.04


import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
df = pd.read_csv('creditCustomers.csv')
cat_feature = 'Card_Category'

num_feature = 'Customer_Age'
plt.figure(figsize=(8, 6))
sns.boxplot(x=cat_feature, y=num_feature, data=df)
plt.xlabel(f'{cat_feature}')
plt.ylabel(f'{num_feature}')
plt.title(f'Boxplot: {num_feature} by {cat_feature}')
plt.grid(True)
plt.show()


num_feature = 'Total_Trans_Amt'

# Calculate mean
mean = df[num_feature].mean()
print(f'Mean of {num_feature}: {mean:.2f}')

# Calculate median
median = df[num_feature].median()
print(f'Median of {num_feature}: {median:.2f}')

# Calculate standard deviation
std = df[num_feature].std()
print(f'Standard Deviation of {num_feature}: {std:.2f}')

Mean of Total_Trans_Amt: 4404.09
Median of Total_Trans_Amt: 3899.00
Standard Deviation of Total_Trans_Amt: 3397.13


# Choose two numerical features for the correlation plot
feature1 = 'Total_Trans_Amt'
feature2 = 'Customer_Age'
#todo zov zurah
# Create the correlation plot
plt.figure(figsize=(8, 6))
sns.regplot(x=feature1, y=feature2, data=df)
plt.xlabel(f'{feature1}')
plt.ylabel(f'{feature2}')
plt.title(f'Correlation Plot: {feature1} vs. {feature2}')
plt.grid(True)
plt.show()


import scipy.stats as stats
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('creditCustomers.csv')

# Choose the numerical feature for the normality check
num_feature = 'Total_Trans_Amt'
# todo normality plot oor solih
# Plot histogram
# Choose the numerical feature for the normality check
num_feature = 'Total_Trans_Amt'

# Create a Q-Q plot
plt.figure(figsize=(8, 6))
stats.probplot(df[num_feature], dist="norm", plot=plt)
plt.title(f'Normal Probability Plot: {num_feature}')
plt.grid(True)
plt.show()

# Calculate skewness
skewness = df[num_feature].skew()
print(f'Skewness of {num_feature}: {skewness:.2f}')

Skewness of Total_Trans_Amt: 2.04


import pandas as pd
import matplotlib.pyplot as plt

# Load your dataset
df = pd.read_csv('creditCustomers.csv')

# Define the numerical feature you want to categorize
num_feature = 'Total_Trans_Amt'

# Create bins for the numerical feature based on its range
max_value = df[num_feature].max()
bins = [0, 6000, 9000, 12000, max_value]

# Use the cut function to create a new categorical column in the DataFrame
df['Category_Feature'] = pd.cut(df[num_feature], bins, labels=['Low', 'Medium', 'High', 'Very High'])

# Now you can group the data by the new 'Category_Feature'
grouped_data = df.groupby('Category_Feature')

# Plot histograms and calculate skewness for each category
for name, group in grouped_data:
    plt.figure(figsize=(8, 6))
    plt.hist(group[num_feature], bins=20, color='skyblue', edgecolor='black')
    plt.xlabel(f'{name} {num_feature}')
    plt.ylabel('Frequency')
    plt.title(f'Histogram of {num_feature} for {name} category')
    plt.grid(True)
    plt.show()

    # Calculate and print skewness for each category
    skewness = group[num_feature].skew()
    print(f'Skewness of {num_feature} for {name} category: {skewness:.2f}')

Skewness of Total_Trans_Amt for Low category: -0.15

Skewness of Total_Trans_Amt for Medium category: -0.28

Skewness of Total_Trans_Amt for High category: 1.82

Skewness of Total_Trans_Amt for Very High category: 0.24


from scipy.stats import shapiro
import pandas as pd

# Choose the numerical feature for the Shapiro-Wilk test
num_feature = 'Total_Trans_Amt'

# Perform the Shapiro-Wilk test
stat, p = shapiro(df[num_feature])
print(f'Shapiro-Wilk Test Statistic: {stat:.4f}')
print(f'Shapiro-Wilk Test P-value: {p:.4f}')

# Interpret the result
alpha = 0.05 # significance level
if p > alpha:
    print('Fail to reject the null hypothesis. The feature is normally distributed.')
else:
    print('Reject the null hypothesis. The feature is not normally distributed.')

Shapiro-Wilk Test Statistic: 0.7465
Shapiro-Wilk Test P-value: 0.0000
Reject the null hypothesis. The feature is not normally distributed.

/Users/apple/anaconda3/lib/python3.11/site-packages/scipy/stats/_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.
  warnings.warn("p-value may not be accurate for N > 5000.")


from scipy.stats import ks_2samp
# Choose two numerical features for the two-sample Kolmogorov-Smirnov test
feature1 = 'Total_Trans_Amt'
feature2 = 'Customer_Age'

# Perform the two-sample Kolmogorov-Smirnov test
stat, p = ks_2samp(df[feature1], df[feature2])
print(f'Two-Sample Kolmogorov-Smirnov Test Statistic: {stat:.4f}')
print(f'Two-Sample Kolmogorov-Smirnov Test P-value: {p:.4f}')

# Interpret the result
alpha = 0.05 # significance level
if p > alpha:
    print('Fail to reject the null hypothesis. The two features have the same distribution.')
else:
    print('Reject the null hypothesis. The two features have different distributions.')

Two-Sample Kolmogorov-Smirnov Test Statistic: 1.0000
Two-Sample Kolmogorov-Smirnov Test P-value: 0.0000
Reject the null hypothesis. The two features have different distributions.

	CLIENTNUM	Customer_Age	Dependent_count	Months_on_book	Total_Relationship_Count	Months_Inactive_12_mon	Contacts_Count_12_mon	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio	Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1	Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
count	1.012700e+04	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000	10127.000000
mean	7.391776e+08	46.325960	2.346203	35.928409	3.812580	2.341167	2.455317	8631.953698	1162.814061	7469.139637	0.759941	4404.086304	64.858695	0.712222	0.274894	0.159997	0.840003
std	3.690378e+07	8.016814	1.298908	7.986416	1.554408	1.010622	1.106225	9088.776650	814.987335	9090.685324	0.219207	3397.129254	23.472570	0.238086	0.275691	0.365301	0.365301
min	7.080821e+08	26.000000	0.000000	13.000000	1.000000	0.000000	0.000000	1438.300000	0.000000	3.000000	0.000000	510.000000	10.000000	0.000000	0.000000	0.000008	0.000420
25%	7.130368e+08	41.000000	1.000000	31.000000	3.000000	2.000000	2.000000	2555.000000	359.000000	1324.500000	0.631000	2155.500000	45.000000	0.582000	0.023000	0.000099	0.999660
50%	7.179264e+08	46.000000	2.000000	36.000000	4.000000	2.000000	2.000000	4549.000000	1276.000000	3474.000000	0.736000	3899.000000	67.000000	0.702000	0.176000	0.000181	0.999820
75%	7.731435e+08	52.000000	3.000000	40.000000	5.000000	3.000000	3.000000	11067.500000	1784.000000	9859.000000	0.859000	4741.000000	81.000000	0.818000	0.503000	0.000337	0.999900
max	8.283431e+08	73.000000	5.000000	56.000000	6.000000	6.000000	6.000000	34516.000000	2517.000000	34516.000000	3.397000	18484.000000	139.000000	3.714000	0.999000	0.999580	0.999990

	CLIENTNUM	Attrition_Flag	Customer_Age	Gender	Dependent_count	Education_Level	Marital_Status	Income_Category	Card_Category	Months_on_book	...	Credit_Limit	Total_Revolving_Bal	Avg_Open_To_Buy	Total_Amt_Chng_Q4_Q1	Total_Trans_Amt	Total_Trans_Ct	Total_Ct_Chng_Q4_Q1	Avg_Utilization_Ratio	Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1	Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
0	768805383	Existing Customer	45	M	3	High School	Married	$60K - $80K	Blue	39	...	12691.0	777	11914.0	1.335	1144	42	1.625	0.061	0.000093	0.99991
1	818770008	Existing Customer	49	F	5	Graduate	Single	Less than $40K	Blue	44	...	8256.0	864	7392.0	1.541	1291	33	3.714	0.105	0.000057	0.99994
2	713982108	Existing Customer	51	M	3	Graduate	Married	$80K - $120K	Blue	36	...	3418.0	0	3418.0	2.594	1887	20	2.333	0.000	0.000021	0.99998
3	769911858	Existing Customer	40	F	4	High School	Unknown	Less than $40K	Blue	34	...	3313.0	2517	796.0	1.405	1171	20	2.333	0.760	0.000134	0.99987
4	709106358	Existing Customer	40	M	3	Uneducated	Married	$60K - $80K	Blue	21	...	4716.0	0	4716.0	2.175	816	28	2.500	0.000	0.000022	0.99998